set more off


local J `1'
local sigma `2'
local sigmax `3'


reshape long x_ z_ epsilon_ , i(ID c alpha beta) j(altID)

rename x_ x
rename z_ z
rename epsilon_ epsilon

*Create choice and open numbers
*Order choices by free utility
*Keep searching if:
*max_{k \in set of searched goods}  alpha*x_{k} + beta*z_{k} + epsilon_{k}   < alpha*x_{j+1} + beta*r + epsilon_{j+1}

*Loop through goods
*One variable is current best
*If current best exceeds benchmark for next good, stop and choose current best
*If current best is less than benchmark, keep going

*At the end of loop, need to tag open boxes and chosen good

gen FU = alpha*x+epsilon
gen fullU = FU+beta*z




*Stop searching if Ubar > c
gsort ID -FU
by ID: gen newID = _n

forvalues x = 1/`J' {
by ID: gen FU_`x' = FU[`x']
by ID: gen U_`x' = fullU[`x']
}

gen open = 1
replace open = 0 if FU < c & newID != 1




gen tempU = fullU-(1-open)*1e20
bys ID: egen maxU = max(tempU)
gen chosen = (tempU == maxU)

bys ID: egen numchosen = sum(chosen)
egen maxchosen = max(numchosen)
local maxchosen = maxchosen
while(`maxchosen' != 1) {
drop numchosen maxchosen
replace tempU = tempU+.00001*uniform()
drop chosen
gen chosen = (tempU == maxU)
bys ID: egen numchosen = sum(chosen)
egen maxchosen = max(numchosen)
local maxchosen = maxchosen
}
drop numchosen maxchosen
